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Objective: Lipoprotein (a) [Lp(a)] is a causal, genetically-inherited risk amplifier for atherosclerotic cardiovascular disease (ASCVD). Practice guidelines increasingly recommend broad Lp(a) screening among various populations to optimize preventive care. Corresponding changes in testing rates and population-level detection of elevated Lp(a) in recent years has not been well described. Methods: Using Veterans Affairs electronic health record data, we performed a retrospective cohort study evaluating temporal trends in Lp(a) testing and detection of elevated Lp(a) levels (defined as greater than 50 mg/dL) from January 1, 2014 to December 31, 2023 among United States Veterans without prior Lp(a) testing. Testing rates were stratified based on demographic and clinical factors to investigate possible drivers for and disparities in testing: age, sex, race and ethnicity, history of ASCVD, and neighborhood social vulnerability. Results: Lp(a) testing increased nationally from 1 test per 10,000 eligible Veterans (558 tests) in 2014 to 9 tests per 10,000 (4,440 tests) in 2023, while the proportion of elevated Lp(a) levels remained stable. Factors associated with higher likelihood of Lp(a) testing over time were a history of ASCVD, Asian race, and residing in neighborhoods with less social vulnerability. Conclusion: Despite a 9-fold increase in Lp(a) testing among US Veterans over the last decade, the overall testing rate remains extremely low. The steady proportion of Veterans with elevated Lp(a) over time supports the clinical utility of testing expansion. Efforts to increase testing, especially among Veterans living in neighborhoods with high social vulnerability, will be important to reduce emerging disparities as novel therapeutics to target Lp(a) become available.

Therapeutic Area: ASCVD/CVD Risk Assessment Background: Coronary artery calcium (CAC) is the strongest predictor of cardiovascular events. CAC can be identified on non-cardiac chest CTs, but reporting is inconsistent. We developed a natural language processing (NLP) algorithm to identify incidental CAC reporting on non-gated chest CT reports and described patterns in CAC reporting across the Veterans Health Administration (VHA). Methods: We identified non-cardiac CT scan reports across the VHA between 2006-2024. We developed an NLP algorithm by creating Regex rules to detect mentions of CAC (none, mild, moderate, severe, or unclassified). We manually annotated 1,060 reports as the gold standard for algorithm development. We iteratively refined an NLP algorithm based on accuracy with the development scans. We validated the algorithm's performance on an independent sample of 1,000 scans and applied the algorithm to all non-cardiac chest CTs in the VHA over the study period. We described the frequency of CAC reporting over time in addition to facility-level variation. Results: Across 1,000 validation reports, the algorithm had a sensitivity of 99% and a positive predictive value (PPV) of 94% for CAC being mentioned in the CT report. Among reports in which CAC was mentioned, the algorithm had a 99% sensitivity and 97% PPV for correctly noting the presence of CAC. The algorithm had a 96% accuracy for correctly detecting the reported CAC severity.There were 6,825,889 non-cardiac chest CTs between January 2006 and March 2024 in the VHA. The presence or absence of CAC was described in 2,519,296 reports (37%). CAC reporting was highest among lung cancer screening CTs (49%). CAC reporting increased over time (Table). In 2023, reporting ranged from 0% to 63% across 128 VA facilities.Among CTs that reported CAC presence or absence, CAC was described as present on 2,425,416 reports (96%). Among CTs that reported CAC presence, CAC severity was unclassified in 56%, mild in 16%, moderate in 13%, and severe in 15% of scans. Conclusions: CAC is not reported on a majority of non-cardiac chest CTs in a large national cohort, but reporting is increasing over time. Strategies to improve CAC reporting or leverage emerging automated CAC detection algorithms are needed.

Abstract BackgroundWith the capability to render prediagnoses, consumer wearables have the potential to affect subsequent diagnoses and the level of care in the health care delivery setting. Despite this, postmarket surveillance of consumer wearables has been hindered by the lack of codified terms in electronic health records (EHRs) to capture wearable use. ObjectiveWe sought to develop a weak supervision–based approach to demonstrate the feasibility and efficacy of EHR-based postmarket surveillance on consumer wearables that render atrial fibrillation (AF) prediagnoses. MethodsWe applied data programming, where labeling heuristics are expressed as code-based labeling functions, to detect incidents of AF prediagnoses. A labeler model was then derived from the predictions of the labeling functions using the Snorkel framework. The labeler model was applied to clinical notes to probabilistically label them, and the labeled notes were then used as a training set to fine-tune a classifier called Clinical-Longformer. The resulting classifier identified patients with an AF prediagnosis. A retrospective cohort study was conducted, where the baseline characteristics and subsequent care patterns of patients identified by the classifier were compared against those who did not receive a prediagnosis. ResultsThe labeler model derived from the labeling functions showed high accuracy (0.92; F1F122PPP ConclusionsOur work establishes the feasibility and efficacy of an EHR-based surveillance system for consumer wearables that render AF prediagnoses. Further work is necessary to generalize these findings for patient populations at other sites.

Background Heart failure disproportionately affects individuals residing in rural areas, leading to worse health outcomes. Digital health interventions have been proposed as a promising approach for improving heart failure management. This systematic review aims to identify randomized trials of digital health interventions for individuals living in underserved rural areas with heart failure. Methods and Results We conducted a systematic review by searching 6 databases (CINAHL, EMBASE, MEDLINE, Web of Science, Scopus, and PubMed; 2000–2023). A total of 30 426 articles were identified and screened. Inclusion criteria consisted of digital health randomized trials that were conducted in underserved rural areas of the United States based on the US Census Bureau's classification. Two independent reviewers screened the studies using the National Heart, Lung, and Blood Institute tool to evaluate the risk of bias. The review included 5 trials from 6 US states, involving 870 participants (42.9% female). Each of the 5 studies employed telemedicine, 2 studies used remote monitoring, and 1 study used mobile health technology. The studies reported improvement in self‐care behaviors in 4 trials, increased knowledge in 2, and decreased cardiovascular mortality in 1 study. However, 3 trials revealed no change or an increase in health care resource use, 2 showed no change in cardiac biomarkers, and 2 demonstrated an increase in anxiety. Conclusions The results suggest that digital health interventions have the potential to enhance self‐care and knowledge of patients with heart failure living in underserved rural areas. However, further research is necessary to evaluate their impact on clinical outcomes, biomarkers, and health care resource use. Registration URL: https://www.crd.york.ac.uk/prospero/; Unique identifier: CRD42022366923.

The evidence of direct oral anticoagulants (DOACs) usage for venous thromboembolism (VTE) in patients at extremes of body weight or mass index is limited. In such situations, warfarin may be more frequently used. We investigated warfarin time in the therapeutic international normalized ratio range (TTR) and DOAC adherence based on the calculated proportion of days covered (PDC) by pill coverage from a DOAC prescription in patients with VTE across all body sizes. Using data from the Veterans Health Administration (VA), we identified first-time patients with VTE between 2013 and 2018 treated with warfarin or DOACs. We analyzed 28,245 patients with warfarin TTR (N = 10,167) or DOAC PDC(N = 18,078). For warfarin-treated patients after index VTE, mean TTR was lower over shorter treatment durations (TTR 30 vs TTR 180 [mean ± SD]: 43.8% ± 33.5% vs 58.8% ± 23.5%). Mean TTR over 180 days after VTE was lowest for patients

Abstract Special Economic Zones (SEZs) have become pivotal instruments for fostering regional development and industrial growth worldwide, yet comprehensive socioeconomic and spatial impacts remain crucial for understanding their true potential. This study aimed to evaluate the impact and identify the challenges of the SEZ in Indonesia, namely Kendal SEZ, by employing a mixed-methods approach to better capture the nuances of the SEZ’s impacts on the surrounding economy. A qualitative analysis was conducted as an exploratory phase involving SEZ management, enterprises, and government representatives. The quantitative analysis used Propensity Score Matching-Difference in Difference (PSM-DiD) and Geospatial Methods. Findings reveal a positive impact on increasing total household expenditures, reflecting enhanced community well-being. This SEZ strengthened the manufacturing sector, as evidenced by the increased location quotient. Spatial analysis reveals heightened economic activity via Night-Time Light emissions. The Kendal SEZ emerged as a driver of local economic growth and advanced industrial development among Indonesia’s SEZ models despite challenges like infrastructure and technical limitations. Our study contributes robust empirical evidence of the direct and indirect impacts of SEZs on regional economic development. We suggest that future policy considerations should address infrastructure and workforce challenges to sustain and enhance the benefits of SEZ.